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Found 28 papers, 7 papers with code
Enhancing wind field resolution in complex terrain through a knowledge-driven machine learning approach
Atmospheric flows are governed by a broad variety of spatio-temporal scales, thus making real-time numerical modeling of such turbulent flows in complex terrain at high resolution computationally intractable.
SuperBench: A Super-Resolution Benchmark Dataset for Scientific Machine Learning
Super-Resolution (SR) techniques aim to enhance data resolution, enabling the retrieval of finer details, and improving the overall quality and fidelity of the data representation.
Digital Twins in Wind Energy: Emerging Technologies and Industry-Informed Future Directions
The contribution of this article lies in its synthesis of the current state of knowledge and its identification of future research needs and challenges from an industry perspective, ultimately providing a roadmap for future research and development in the field of digital twin and its applications in the wind energy industry.
Artificial intelligence-driven digital twin of a modern house demonstrated in virtual reality
By understanding the capability level of a digital twin, we can better understand its potential and limitations.
Prospects of federated machine learning in fluid dynamics
Physics-based models have been mainstream in fluid dynamics for developing predictive models.
Decentralized digital twins of complex dynamical systems
In this paper, we introduce a decentralized digital twin (DDT) framework for dynamical systems and discuss the prospects of the DDT modeling paradigm in computational science and engineering applications.
Variational multiscale reinforcement learning for discovering reduced order closure models of nonlinear spatiotemporal transport systems
A central challenge in the computational modeling and simulation of a multitude of science applications is to achieve robust and accurate closures for their coarse-grained representations due to underlying highly nonlinear multiscale interactions.
Digital Twin Data Modelling by Randomized Orthogonal Decomposition and Deep Learning
Associating the dynamical process with a digital twin model of reduced complexity has the significant advantage to map the dynamics with high accuracy and reduced costs in CPU time and hardware to timescales over which that suffers significantly changes and so it is difficult to explore.
Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach
In the current work, we demonstrate how a hybrid approach combining the best of PBM and DDM can result in models which can outperform them both.
Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling
We propose a new physics guided machine learning (PGML) paradigm that leverages the variational multiscale (VMS) framework and available data to dramatically increase the accuracy of reduced order models (ROMs) at a modest computational cost.
Physics guided neural networks for modelling of non-linear dynamics
The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention.
Risk-based implementation of COLREGs for autonomous surface vehicles using deep reinforcement learning
Autonomous systems are becoming ubiquitous and gaining momentum within the marine sector.
Nonlinear proper orthogonal decomposition for convection-dominated flows
Autoencoder techniques find increasingly common use in reduced order modeling as a means to create a latent space.
Deep neural network enabled corrective source term approach to hybrid analysis and modeling
In this work, we introduce, justify and demonstrate the Corrective Source Term Approach (CoSTA) -- a novel approach to Hybrid Analysis and Modeling (HAM).
Hybrid analysis and modeling, eclecticism, and multifidelity computing toward digital twin revolution
Most modeling approaches lie in either of the two categories: physics-based or data-driven.
Geometric Change Detection in Digital Twins using 3D Machine Learning
Both stand-alone and descriptive digital twins incorporate 3D geometric models, which are the physical representations of objects in the digital replica.
On the effectiveness of signal decomposition, feature extraction and selection on lung sound classification
A vital part of using the lung sound for disease detection is discrimination between normal lung sound and abnormal lung sound.
Sound Audio and Speech Processing
Physics guided machine learning using simplified theories
Recent applications of machine learning, in particular deep learning, motivate the need to address the generalizability of the statistical inference approaches in physical sciences.
A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction
We put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements.
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles
Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems.
Interface learning of multiphysics and multiscale systems
Complex natural or engineered systems comprise multiple characteristic scales, multiple spatiotemporal domains, and even multiple physical closure laws.
COLREG-Compliant Collision Avoidance for Unmanned Surface Vehicle using Deep Reinforcement Learning
Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics.
Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning
In this paper, we put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements.
Dynamical Systems Computational Physics Fluid Dynamics
A forward sensitivity approach for estimating eddy viscosity closures in nonlinear model reduction
In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models.
Dynamical Systems Fluid Dynamics
Marine life through You Only Look Once's perspective
In an effort to preserve maritime wildlife the Norwegian government has decided that it is necessary to create an overview over the presence and abundance of various species of wildlife in the Norwegian fjords and oceans.
Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning
In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions with non-moving obstacles along the way.
A long short-term memory embedding for hybrid uplifted reduced order models
In the first layer, we utilize an intrusive projection approach to model dynamics represented by the largest modes.
Fluid Dynamics Dynamical Systems Computational Physics
Dissecting Deep Neural Networks
It has been shown that neural networks with piecewise affine activation functions are themselves piecewise affine, with their domains consisting of a vast number of linear regions.
